This disclosure relates to controlling an electronic device by generating an automated schedule tailored to preferences revealed by user behavior.
This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present techniques, which are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art.
People interact with many electronic devices in their daily lives. Many of these electronic devices are “smart” devices that can be controlled remotely or according to a schedule. Smart lighting, for example, may be controlled to be raised or lowered to set an appropriate mood. Likewise, a smart thermostat may manage a thermal environment of a structure, such as a residential or commercial building. A smart thermostat may decide how to condition the structure properly, which may include varying an internal temperature, humidity, and/or other environmental characteristic based on a setpoint schedule and/or temperatures selected by a person interacting with the thermostat.
Some smart devices may even learn from the way people control them. For example, some smart thermostats have applied an individual rules-and-exceptions-based learning approach to automatically generate temperature setpoint schedules. The rules-and-exceptions-based learning approach may involve observing interactions with the smart thermostat over time and, based on certain defined rules and exceptions, determine whether the interactions have some meaning that should be used to build a temperature setpoint schedule. In one example, the rules-and-exceptions-based learning approach may determine to include a temperature setpoint in a setpoint schedule when a person interacts with the thermostat to consistently select a similar temperature for several days in a row, or for the same day of the week for several weeks in a row, at about the same time of day, but not when the person selects a different temperature at about that time of day during an intervening day. Because there are numerous possible scenarios that could explain whether the person would want a temperature change to add or remove a temperature setpoint of an automated temperature setpoint schedule, the rules-and-exceptions-based learning approach may include a large number of rules and exceptions.
While a rules-and-exceptions-based learning approach may allow a smart device to generally learn what to do based on the person's interactions with the smart device, this may not be the case if the interactions happen not to match a defined rule or exception. As a result, the rules-and-exceptions-based learning approach may sometimes produce errant results. For instance, the rules-and-exceptions-based learning approach may too heavily consider interactions that occur soon after the smart device is installed, may produce setpoint schedules that are inefficient, or may change setpoints too often or too infrequently to effectively account for occupant preferences.